Recently, deep learning (DL) has been successfully applied to various scenarios in communications.
It has been shown that DL can provide significant performance improvement for signal processing and communication problems,
such as decoding, demodulation, and detection [1]} [2]}.
In [3]}, an end-to-end detector based on DNN was trained by treating the orthogonal frequency division multiplexing (OFDM) modulation and wireless channel as a black box.
The received signals, including a pilot block and a data block, are fed to the black-box DNN as input. The network outputs estimates of the transmitted signals.
Further in [4]}, a DL method, namely Detection Network (DetNet), was proposed by following the calculation procedure of gradient-based optimization.
The DetNet is devised with low computational complexity and tries to approach the optimal performance.
A sphere decoding algorithm based on DL was studied in [5]} where the radius of the decoding hypersphere is learned by a DNN prior to decoding.
In [6]}, the existing deep autoencoder for MIMO was improved by introducing singular-value decomposition (SVD) as a differentiable layer in the network.
However, the above DL-based methods may experience significant delay due to the need for online collection of sufficient training data [7]}.
In addition, the long training time makes these methods prohibitive from adapting to time-varying communication scenarios.
